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Sdenka Zobeida Salas-Pilco Yuqin Yang 《British journal of educational technology : journal of the Council for Educational Technology》2020,51(4):875-891
This study presents several Latin American research initiatives in the field of learning analytics (LA). The study’s purpose is to enhance awareness and understanding of LA among researchers, practitioners and decision makers, and to highlight the importance of supporting research on LA. We analyzed case studies of LA research conducted at four levels of the educational system (the national, institutional, classroom and student levels), which were implemented in four countries (Brazil, Ecuador, Mexico and Uruguay). Diversified cases were selected to demonstrate the use of LA in primary, secondary and higher education, and to allow the inclusion of different types of datasets. These cases also showed the development of legal frameworks for handling ethical issues, and they met the requirements for data privacy protection in Latin America. The study concludes with a discussion of the findings and their implications for further research and practice in the field of LA for teaching and learning. 相似文献
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Sdenka Zobeida Salas-Pilco 《British journal of educational technology : journal of the Council for Educational Technology》2020,51(5):1808-1825
This qualitative study examines the use of artificial intelligence (AI) and robotics in learning designs from the perspective of learning sciences. The literature on the topic indicates that there is not enough research on including diverse learning outcomes in the designs for learning. Therefore, the purpose of this study was to understand how AI and robots impact physical, social-emotional and intellectual learning outcomes through the implementation of learning designs that are guided by selected design principles. In this study, the design-based research (DBR) methodology was employed for investigating learning in naturalistic contexts. The intervention was implemented in a primary school in which learners used educational robots. The main findings reveal that the development of an integrated analytical framework, which considers a broader spectrum of human potential, allows for analyzing students’ learning outcomes in a more integral, inclusive and balanced way. This, in turn, promotes students’ learning by using AI and robots. Another finding reveals that the impact of using AI and robotics on learning designs is reflected in learners’ personal trajectories having different pathways and paces. Finally, the lessons learned and the challenges to be overcome are summarized, and recommendations are made for future research for the enhancement of learning experiences that use AI and robotics. 相似文献
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Yuqin Yang Zhizi Zheng Gaoxia Zhu Sdenka Zobeida Salas-Pilco 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(4):1025-1045
Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research explored how an analytics-supported reflective assessment (AsRA)-enhanced KB design influenced 6th graders' KB and data science practices in a science education setting. One intact class with 56 students participated in this study. The analysis of students' Knowledge Forum discourse showed the positive influences of the AsRA-enhanced KB design on students' development of KB and data science practices. Further analysis of different-performing groups revealed that the AsRA-enhanced KB design was accessible to all performing groups. These findings have important implications for teachers and researchers who aim to develop students' KB and data science practices, and general high-level collaborative inquiry skills.
Practitioner notes
What is already known about this topic- Data use becomes increasingly important in the K-12 educational context.
- Little is known about how to scaffold students to develop data science practices.
- Knowledge Building (KB) and learning analytics-supported reflective assessment (AsRA) show premises in developing these practices.
- AsRA-enhanced KB can help students improve KB and data science practices over time.
- AsRA-enhanced KB design benefits students of different-performing groups.
- AsRA-enhanced KB is accessible to elementary school students in science education.
- Developing a collaborative and reflective culture helps students engage in collaborative inquiry.
- Pedagogical approaches and analytic tools can be developed to support students' data-driven decision-making in inquiry learning.
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